Manufacturers using AI and Machine Learning reap the benefits of improved efficiency through proactive, streamlined operations. Delta Bravo delivers on the following manufacturing use cases today.
PREDICTIVE MAINTENANCE
According the International Society of Automation, a typical factory loses between 5% and 20% of its manufacturing capacity due to downtime. Delta Bravo leverages data and context to generate more accurate predictions of the lifespan for a component given environmental conditions. When specific failure signals are observed, or component aging criteria is projected, Delta Bravo forecasts this to manufacturing operations, giving them several weeks advanced notice of when components should be replaced. McKinsey and Company found that AI based predictive maintenance typically generates a 10% reduction in annual maintenance costs, up to a 25% downtime reduction and a 25% reduction in inspections costs.
Case Study:
Delta Bravo is currently working with a global manufacturer with over 4,000 machines in 250 client sites worldwide. We’re helping the manufacturer forecast maintenance events and proactively train client operators to ensure higher uptime and longer machine life.
SUPPLY CHAIN OPTIMIZATION
Delta Bravo leverages historical data, environmental data and recent trends to predict optimal resource needs at each stage of production. We can identify anomalies in requests, resource utilization and more to help supply chain managers determine optimal inventory levels versus expected sales. This information is used to optimize resource plans, reroute inventory where it is needed, and streamline resources for better ongoing efficiency. McKinsey predicts machine learning will reduce supply chain forecasting errors by 50% and reduce lost sales by 65% with better product availability.
Case Study:
Delta Bravo is working with AccuWeather, Microsoft and several manufacturers to leverage weather and other data sources to forecast inventory needs for large customers. Machine learning is being used to predict inventory fluctuations tied to weather anomalies, traffic events and other variables not considered in traditional supply chain models.
PREDICTIVE PROCESS DESIGN
Combining real-time monitoring and machine learning is optimizing process design, providing insights into machine-level loads and production schedule performance. Knowing in real-time how each machine’s load level impacts overall production schedule performance leads to better decisions managing each production run. Optimizing the best possible set of machines for a given production run is now possible using machine learning algorithms.
Case Study:
Delta Bravo recently completed an engagement that traced the root cause of component waste to a particular machine process. A machine learning model was applied to this process, enabling the manufacturer to predict if the process would have an 85% or better chance of success. If lower than 85%, the process was terminated and restarted. This reduced scrap rates, saving the manufacturer tens of thousands of dollars per month in wasted materials and improved time-to-production metrics.